Association between Glycemic Management during Pregnancy and Postpartum Metabolic Health Outcomes among Women with Gestational Diabetes Mellitus

Association between Glycemic Management during Pregnancy and Postpartum Metabolic Health Outcomes among Women with Gestational Diabetes Mellitus | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between Glycemic Management during Pregnancy and Postpartum Metabolic Health Outcomes among Women with Gestational Diabetes Mellitus Tianli Zhu, Yujie Dong, Ziwei Ding, Le Wang, Lanfang Zhao, Tuyan Fan, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6639941/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Glycemic management is standard for gestational diabetes mellitus (GDM), yet its impact on postpartum metabolic health, especially in late-onset GDM, remains uncertain. Objective To assess whether achieving glycemic control during pregnancy improves postpartum metabolic health compared to those who do not. Methods From June 2021 to December 2022, 358 gestational diabetes mellitus (GDM) cases and 750 controls were recruited at 24~28 weeks of gestation from Ma'anshan Maternal and Child Health Care Center, China. Participants were categorized into four groups based on third-trimester fasting plasma glucose (FPG): 1) Non-GDM, 2) GDM with normal glycemic control (FPG < 5.1 mmol/L), 3) GDM with abnormal glycemic control, and 4) late-onset GDM. Follow-ups at 42 days and 1 year postpartum included questionnaires, physical examinations, and metabolic measurements. Multivariate regression analyzed associations between glycemic control and postpartum outcomes. Results Among 642 and 736 participants followed at 42 days and 1 year postpartum, GDM with abnormal glycemic control had increased risks of blood glucose ( OR = 5.22, [95% CI 1.66 to 16.38], P =0.005) and TG abnormalities ( OR = 2.43, [95% CI 1.01 to 5.85], P =0.048). No significant associations were found for GDM with normal glycemic control or late-onset GDM. Compared to GDM with normal control, abnormal control increased risks of 2-hour glucose ( OR = 2.77, [95% CI 1.02 to 7.53], P =0.045) and TC abnormalities ( OR = 2.97, [95% CI 1.08 to 8.18], P =0.035). Conclusions Glycemic management during pregnancy improves postpartum metabolic outcomes, highlighting the importance of GDM diagnosis and subsequent glycemic control. Gestational diabetes mellitus Glycemic management during pregnancy Postpartum Metabolic indicators Figures Figure 1 Figure 2 Figure 3 Highlights Why did we undertake this study? Gestational diabetes mellitus (GDM) is associated with an increased risk of adverse maternal metabolic outcomes postpartum. However, the benefits of glycemic management during pregnancy on maternal postpartum metabolic health remain unclear, particularly in women with late-onset GDM. What is the specific question(s) we wanted to answer? Does achieving glycemic control during pregnancy improve postpartum metabolic health outcomes? How do postpartum outcomes differ between women with late-onset GDM and those diagnosed earlier in pregnancy? What did we find? - Women with GDM who failed to meet glycemic control targets during pregnancy exhibited significantly worse postpartum metabolic outcomes, including higher fasting plasma glucose (FPG), total cholesterol (TC), and triglyceride (TG) levels. - Poor glycemic control during pregnancy also increased the risk of postpartum glucose and lipid abnormalities up to 1 year after delivery. - No significant associations were observed in women with late-onset GDM, suggesting different underlying mechanisms. What are the implications of our findings? Our findings underscore the importance of early diagnosis of GDM and achieving glycemic control during pregnancy to improve postpartum metabolic health. These results support strengthening clinical management strategies and tailoring interventions for GDM, particularly in women with varying onset timings. 1 Introduction Gestational diabetes mellitus (GDM) refers to impaired glucose tolerance identified for the first time during pregnancy, occurring without any prior diagnosis of diabetes. GDM ranks among the most prevalent complications of pregnancy, impacting roughly 14% of women globally 1 , 2 . It is widely accepted that GDM has both short-term and long-term health risks on both mothers and their offspring 3 , 4 . Therefore, identifying effective strategies to mitigate the risks of adverse health outcomes is crucial. In recent years, substantial revisions to the diagnostic criteria for GDM have resulted in a significant rise in the number of women identified with this condition 5 , 6 . This trend has placed unprecedented strain on the healthcare system, increasing resource consumption and costs, while also imposing substantial psychological and financial burdens on individuals and their families 7 . While relationship between GDM and adverse maternal-fetal pregnancy outcomes, as well as long-term health risks have been well documented 3 , a key question remains: could women and their offspring benefit from the subsequent interventions after being identified under these broader diagnostic criteria? American Diabetes Association emphasizes the placement of blood glucose control at the forefront of GDM management during pregnancy 8 . Research has demonstrated that controlling maternal hyperglycemia through appropriate treatment can lower the likelihood of large-for-gestational-age infants and enhance various key maternal and perinatal health outcomes 9 , 10 . However, regarding maternal postpartum health outcomes, only one randomized controlled trial demonstrated dietary management, monitoring blood glucose levels and administering insulin therapy (if necessary) could significantly reduce incidence of postpartum depressive symptoms (control group: 50%, intervention group: 23%) 9 . While no study has demonstrated if glycemic control during pregnancy could bring favorable maternal long-term metabolic outcomes. Additionally, the adverse effects of late-onset GDM is of great research interesting, which refers to those who have normal glucose during the second trimester but have abnormal blood glucose in the third trimester 11 . A meta-analysis has shown that late-onset GDM is linked to a higher likelihood of unfavorable perinatal outcomes 12 , however, its impact on long-term maternal metabolic health has not been previously documented. Therefore, based on an ongoing cohort study in China which includes 1108 women, we categorized women into four groups according to their GDM diagnosis status in the second trimester and the glycemic control status recorded during the third trimester and compared their postpartum metabolic indicators to assess if the postpartum metabolic health outcomes of individuals diagnosed with GDM who met the glycemic control target (including those with late-onset GDM) during pregnancy triumph over those not. The ultimate goal is to provide crucial scientific evidence for the formulation of clinical practice guidelines and public health policies. 2 Methods 2.1 Study Participants Our current study was conducted as part of an ongoing GDM Specialized cohort study, which recruited participants between June 2021 and December 2022 at the Ma’anshan Maternal and Child Health Care Center in Ma’anshan City, Anhui Province, China. The study aims to evaluate the impact of GDM on perinatal and long-term postnatal health outcomes, as well as to investigate the role of genetic and environmental risk factors in the development of GDM and its progression to metabolic diseases. Between 24–28 weeks of gestation, women diagnosed with GDM and those not were enrolled at a ratio of 1:2, and a total 1108 women were included, of which 358 were GDM cases and 750 were controls. Participants were routinely followed up at 32 weeks of gestation, 42 days postpartum, 1 year postpartum, and annually thereafter. The study included participants who met the following criteria: (1) gestational age between 24 and 28 weeks; (2) age of 18 years or older; (3) no communication barriers and the ability to independently complete a questionnaire; and (4) willingness to attend prenatal check-ups and deliver at the Ma’anshan Maternal and Child Health Care Center. Participants were excluded if they had visual or cognitive impairments or were involved in other ongoing clinical studies. The study received approval from the Ethics Committee of Anhui Medical University (Approval No. 20210732). All procedures adhered to relevant ethical standards and regulations. Written informed consent was obtained from all participants, and their personal information was handled with strict confidentiality. 2.2 Criteria for hyperglycaemia GDM was diagnosed between 24 and 28 weeks of gestation using a 75-g oral glucose tolerance test (OGTT). The diagnosis was made when any of the following criteria was met: fasting plasma glucose (FPG) ≥ 5.1 mmol/L, 1-hour post-glucose plasma glucose (PG) ≥ 10.0 mmol/L, or 2-hour post-glucose PG ≥ 8.5 mmol/L 13 . In late pregnancy (around 38 weeks), evaluations were conducted in accordance with the Chinese guidelines for the prevention and management of type 2 diabetes, FPG ≥ 5.1 mmol/L is considered as abnormal glycemia in GDM 14 . If blood glucose is normal in early and mid-pregnancy but exceeds 5.1 mmol/L in late pregnancy 11 , the participant was considered as late-onset GDM in our study. Mid- and late-pregnancy blood glucose data for participants were extracted from the electronic medical record system. 2.3 Criteria for Subgroups Participants were categorized into four groups based on the criteria for GDM and following glycemic management in late pregnancy, as specified in the Chinese guidelines for the prevention and management of T2DM. A threshold of 5.1 mmol/L was used to distinguish between normal and abnormal glycemic control 14 . The groups were as follows: (1) Non-GDM with normal glycemic control (control group) (FPG < 5.1 mmol/L); (2) GDM with normal glycemic control (FPG < 5.1 mmol/L); (3) GDM with abnormal glycemic control (FPG ≥ 5.1 mmol/L); and (4) Late-onset GDM (FPG ≥ 5.1 mmol/L during the third trimester). 2.4 Outcomes Women diagnosed with GDM take a 75-g OGTT 42 days postpartum. Outcomes at 42 days postpartum was fasting plasma glucose [FPG], 1-hour post-glucose plasma glucose [1h PG], and 2-hour post-glucose plasma glucose [2h PG] and outcomes at 1 year postpartum were FPG, total cholesterol [TC], triglycerides [TG], high-density lipoprotein [HDL], low-density lipoprotein [LDL], apolipoprotein A [ApoA], and apolipoprotein B [ApoB] and physical examination results including diastolic blood pressure (DBP), systolic blood pressure (SBP), hip circumference, and waist circumference. Biochemical indicators (blood glucose and blood lipids) were retrieved from the electronic medical record system, while physical indicators were collected on-site by the cohort working staff. Blood pressure was assessed by employing a conventional mercury sphygmomanometer through the cuff inflation technique. Prior to measurement, participants were instructed to rest quietly for at least 10 minutes in a calm and relaxed state. Blood pressure was recorded on the right upper arm while the participant remained seated. Measurements were taken twice consecutively, with an interval of at least 30 seconds between each measurement. The individual’s blood pressure value was determined by calculating the average of the two measurements. An abnormal metabolic condition during the postpartum period was characterized as follows: (1) Abnormal blood glucose: FPG ≥ 5.6 mmol/L, 2h PG ≥ 7.8 mmol/L 14 ; (2) Abnormal lipid levels were identified based on the criteria outlined in the 2016 Guidelines for the Prevention and Control of Dyslipidemia in Adults in China 15 . These abnormalities were categorized into four distinct types: 1) hypercholesterolemia, defined as TC ≥ 5.2 mmol/L; 2) hypertriglyceridemia, defined as TG ≥ 1.7 mmol/L; 3) low high-density lipoproteinemia, where HDL ≤ 1.0 mmol/L; and 4) high low-density lipoproteinemia, characterized by LDL ≥ 3.4 mmol/L. An individual had an abnormal level of glucose in any period would be categorized as had abnormal glucose. And the blood lipid abnormality is defined as any of the lipid indicators exceeds the normal range. 2.5 Covariates Self-administered questionnaires guided by trained interviewers were used to collect comprehensive information at baseline and during follow-up. Data collected included maternal socio-demographic characteristics and lifestyle factors during pregnancy, such as maternal age, pre-pregnancy body mass index (BMI), and educational attainment (categorized as middle school or lower, high school, junior college, or higher), parity (0 or ≥ 1), place of residence (urban or rural), smoking and alcohol consumption during pregnancy (yes or no), family history of diabetes or cardiovascular disease (CVD) (immediate family members, typically parents), and physical activity. The level of physical activity was evaluated through the use of the International Physical Activity Questionnaire (IPAQ) 16 , a validated and reliable tool, with results categorized as low, moderate, or high activity levels. Red meat intake was measured using the Dietary Frequency Questionnaire (Food Frequency Questionnaire, FFQ) 17 , which records the frequency (daily, weekly, monthly, or yearly), number of servings, and grams of food intake. OGTT, the OGTT values measured between the 24 and 28 weeks of gestation, including FPG, 1h PG, and 2h PG. Pre-pregnancy BMI, calculated as [weight (kg) / height (m) 2 ], was derived from self-reported measurements of weight and height. prior to pregnancy. Covariates were selected based on the results of univariable analyses ( P < 0.05) and prior literature 18 , 19 demonstrating their significant associations with outcomes such as GDM and CVD. 2.6 Statistical Analysis For continuous variables with a normal distribution, data were presented as the mean ± standard deviation, and comparisons of baseline demographic and clinical characteristics between independent samples were conducted using t-tests. Count data were presented as frequencies (%) and compared between groups using the chi-square (χ 2 ) test. Multivariate linear and logistic regression were applied wherever was appropriate to assess the associations of glycemic management status during pregnancy with postpartum physical (waist and hip circumference) and metabolic indicators (blood pressure, FPG, TC, TG, HDL, LDL, ApoA, and ApoB). Adjusted factors were selected based on previous literature 18 , 19 , including maternal age, pre-pregnancy BMI, education level, marital status, ethnicity, parity, family history of diabetes, family history of cardiovascular disease, smoking (pre-pregnancy and at 1 year postpartum), alcohol consumption (pre-pregnancy and at 1 year postpartum), and physical activity and red meat intake at 42 days and 1 year postpartum. We further conducted a comparative analysis between GDM with abnormal glycemic control and GDM with normal glycemic control. A 2-sided P value of less than 0.05 was set as the level of significance. Statistical analyses were conducted with R 4.2.2, GraphPad Prism 8, and SPSS 23.0 software. 3 Results 3.1 Study Participant Disposition At baseline, we included a total of 1108 women with a mean age of 30.85 (3.92) years, among which 358 (32.3%) were GDM cases and 750 (67.7%) were Non-GDM controls. Comparing the basic characteristics of the two groups, we found that the mean age (31.94 (3.71) years VS 30.33 (3.90) years), the mean BMI ( 23.09 (3.38) kg/m 2 VS 21.77 (3.19) kg/m 2 ), and the percentage of family history of diabetes mellitus (32.4% VS 22.7%) in the GDM group were higher than that in the Non-GDM group ( P < 0.001). No significant differences were observed in other characteristics such as educational level, smoking and alcohol consumption. Detailed characteristics of participant are presented in Table 1 . Table 1 Comparison of basic information of study subjects at baseline Characteristics Overall Non-GDM GDM t/χ2 P- Value (n = 1108) (n = 750) (n = 358) Age (years) 30.85 (3.92) 30.33 (3.90) 31.94 (3.71) -6.52 < 0.001* BMI (kg/m 2 ) 22.20 (3.31) 21.77 (3.19) 23.09 (3.38) -6.34 < 0.001* Ethnicity (%) 0.05 0.950 Han individuals 1089 (98.0) 737 (98.3) 352 (98.3) Others 19 (2.0) 13 (1.7) 6 (1.7) Marital status (%) 0.51 0.470 Married 1095 (99.0) 740 (98.7) 355 (99.2) Others 13 (1.0) 10 (1.3) 3 (0.8) Education level (%) 2.42 0.491 Primary school 9 (1.0) 7 (0.9) 2 (0.6) Middle school 173 (15.9) 109 (14.5) 64 (17.9) High school 230 (20.9) 157 (20.9) 73 (20.4) Junior college or above 696 (63.2) 477 (63.6) 219 (61.2) Nature of work (%) 1.50 0.473 Mental labor 601 (54.1) 406 (54.2) 195 (54.5) Manual labor 73 (6.9) 54 (7.2) 19 (5.3) Unemployed 433 (39.0) 289 (38.6) 144 (40.2) Annual household income (yuan) 6.80 0.147 300, 000 47 (3.9) 26 (3.5) 21 (5.9) Smoking (%) 2.46 0.292 No 1066 (96.2) 724 (96.5) 342 (95.5) Yes 42 (3.8) 26 (3.5) 16 (4.5) Drinking (%) 1.83 0.609 Never 926 (83.6) 621 (82.8) 305 (85.2) Sometimes 129 (11.6) 92 (12.3) 37 (10.3) Often 4 (0.4) 2 (0.3) 2 (0.6) Always 49 (4.4) 35 (4.7) 14 (3.9) Secondhand smoke (%) 0.46 0.928 Never 503 (45.4) 339 (45.2) 164 (45.8) Sometimes 452 (40.8) 304 (40.5) 148 (41.3) Often 118 (10.6) 82 (10.9) 36 (10.1) Always 35 (3.2) 25 (3.3) 10 (2.8) History of adverse pregnancy (%) 0.04 0.847 No 398 (35.9) 261 (34.8) 137 (38.3) Yes Missing values 153 (13.8) 557 (50.3) 99 (13.2) 390 (52.0) 54 (15.1) 167 (46.6) Family history of diabetes (%) 11.99 < 0.001* No 822 (74.2) 580 (77.3) 242 (67.6) Yes 286 (25.8) 170 (22.7) 116 (32.4) Family history of cardiovascular disease (%) 0.27 0.606 No 861 (77.8) 580 (77.3) 281 (78.7) Yes 246 (22.2) 170 (22.7) 76 (21.3) Data were presented as mean (standard deviation) for continuous variables and n (%) for categorical variables. Abbreviations: BMI, pre-pregnancy body mass index. *: P < 0.05. Nature of work, annual household income and family history of cardiovascular disease were missing one person respectively. History of adverse pregnancy was collected at a later stage, so there was no data on the previous period. By the late stage of pregnancy, we successfully collected blood glucose data from a total of 989 women. Subsequently, during the 42-day postpartum follow-up, we collected 642 questionnaires and successfully obtained blood glucose data from 466 participants. Further tracking until one year postpartum, 736 individuals completed questionnaires, 617 individuals completed a physical examination, 583 completed blood pressure measurements, and 376 underwent comprehensive blood glucose and lipid testing (for details, see Fig. 1 ). The mean follow-up duration at 42 days postpartum was 44.51 (7.17) days. The overall abnormality rate of blood glucose levels postpartum is 9.3%. The blood glucose abnormality rate at 42 days postpartum was 10.8% in the GDM group and 6.0% in the Non-GDM group ( P = 0.054). The mean follow-up duration at 1 year postpartum was 442.52 (88.63) days, and the blood glucose abnormality rates were 9.7% and 3.7% in the GDM and Non-GDM groups, respectively ( P = 0.098), and the blood lipid abnormality rate was 16.7% and 15.3% in the two groups ( P = 0.510). Among them, the abnormality rate for TC, TG, HDL, and LDL were 16.1%, 15.2%, 7.3%, and 13.7%. And the TC, TG, HDL, LDL abnormality rates were (14.6% & 18.7%, P = 0.290), (10.0% & 24.0%, P < 0.001), (5.4% & 10.6%, P = 0.049), (12.4% & 16.0%, P = 0.301) in the GDM and Non-GDM groups. 3.2 Comparing Postnatal Metabolic Outcomes between Glycaemic Control Status during Pregnancy In the fully adjusted model, linear regression analysis indicated that compared to the Non-GDM, women diagnosed with GDM who had abnormal glycemia during pregnancy had higher FPG ( β = 0.24, [95% CI 0.05 to 0.43], P = 0.012) at 42-day postpartum, and elevated TC ( β = 0.37, [95% CI 0.05 to 0.69], P = 0.023) and TG ( β = 5.42, [95% CI 0.57 to 10.26], P = 0.029) levels at 1-year postpartum. No significant elevated level of any indicators were observed among women with late-onset GDM (Fig. 2 and Table S1 ). Besides, compared with GDM with normal glycemia control during pregnancy, GDM who had abnormal glycemia control had elevated TC ( β = 0.48, [95% CI 0.16 to 0.80], P = 0.003) and LDL ( β = 0.30, [95% CI 0.04 to 0.56], P = 0.024) levels at 1-year postpartum. Logistic regression analysis revealed that, compared to the Non-GDM, women diagnosed with GDM having abnormal blood glucose control during pregnancy had an increased risk of postpartum (42 days and 1 year combined) blood glucose abnormality ( OR = 5.22, [95% CI 1.66 to 16.38], P = 0.005) and a higher risk of postpartum TG abnormality ( OR = 2.43, [95% CI 1.01 to 5.86], P = 0.048). Similarly, no significant increased risk of any metabolic abnormality were observed among women with late-onset GDM (Fig. 3 and Table S2). Compared with GDM with normal glycemia control during pregnancy, GDM who had abnormal glycemia had increased risks of 42 days postpartum 2h PG abnormality ( OR = 2.77, [95% CI 1.02 to 7.53], P = 0.045) and 1 year postpartum TC abnormality ( OR = 2.97, [95% CI 1.08 to 8.18], P = 0.035) (Table 2 ). Table 2 Association between glycemic control and postpartum metabolic outcomes among women with GDM Indicators GDM, normal glycaemic control GDM, abnormal glycaemic control Model 1 Model 2 Model 2+ β (95%CI) P β (95%CI) P OR (95%CI) P 42 days postpartum FPG (n = 187) 119 (4.98 ± 0.54) 68 (5.11 ± 0.68) 0.13 (-0.05, 0.30) 0.154 0.11 (-0.09, 0.32) 0.279 2.59 (0.86, 7.76) 0.089 1h PG (n = 124) 80 (8.35 ± 1.51) 44 (8.22 ± 1.79) -0.13 (-0.73, 0.47) 0.672 0.00 (-0.75, 0.76) 0.993 —— —— 2h PG (n = 113) 76 (6.78 ± 1.63) 37 (7.09 ± 1.72) 0.32 (-0.34, 0.97) 0.346 0.67 (-0.18, 1.52) 0.121 2.77 (1.02, 7.53) 0.045* 1 year postpartum FPG (n = 140) 83 (4.74 ± 0.68) 57 (4.88 ± 0.62) 0.15 (-0.06, 0.35) 0.158 0.11 (-0.12, 0.34) 0.329 2.25 (0.52, 9.79) 0.281 SBP (n = 194) 119 (108.48 ± 12.24) 75 (109.93 ± 13.63) 1.45 (-2.11, 5.02) 0.423 0.72 (-3.24, 4.68) 0.721 4.20 (0.28, 64.83) 0.304 DBP (n = 194) 119 (73.76 ± 9.85) 75 (77.02 ± 8.74) 3.26 (0.60, 5.93) 0.016* 2.50 (-0.42, 5.43) 0.093 0.91 (0.21, 3.89) 0.894 Waist (n = 209) 128 (71.43 ± 25.18) 81 (74.12 ± 22.87) 2.69 (-3.76, 9.14) 0.413 1.21 (-5.99, 8.41) 0.741 —— —— Hips (n = 209) 128 (86.75 ± 29.86) 81 (90.39 ± 26.56) 3.63 (-3.96, 11.23) 0.348 2.25 (-6.42, 10.92) 0.609 —— —— TC (n = 140) 83 (4.31 ± 0.80) 57 (4.77 ± 0.95) 0.46 (0.18, 0.73) 0.001* 0.48 (0.16, 0.80) 0.003 * 2.97 (1.08, 8.18) 0.035* TG (n = 140) 83 (1.22 ± 0.67) 57 (4.71 ± 2.42) 3.49 (-0.49, 7.46) 0.085 3.20 (-1.84, 8.23) 0.212 1.55 (0.63, 3.79) 0.338 HDL (n = 140) 83 (1.36 ± 0.22) 57 (1.35 ± 0.25) -0.01 (-0.10, 0.07) 0.731 0.00 (-0.09, 0.08) 0.97 0.77 (0.19, 3.04) 0.710 LDL (n = 140) 83 (2.58 ± 0.72) 57 (2.87 ± 0.70) 0.29 (0.06, 0.53) 0.014* 0.30 (0.04, 0.56) 0.024 * 1.65 (0.58, 4.64) 0.347 ApoA (n = 140) 83 (1.06 ± 0.24) 57 (1.09 ± 0.23) 0.03 (-0.05, 0.11) 0.447 0.02 (-0.07, 0.10) 0.675 —— —— ApoB (n = 140) 83 (0.73 ± 0.20) 57 (0.80 ± 0.20) 0.07 (0.01, 0.14) 0.032* 0.07 (-0.00, 0.14) 0.055 —— —— Abbreviation: FPG, fasting plasma glucose; 1h PG, 1-hour post-glucose plasma glucose; 2h PG, 2-hour post-glucose plasma glucose; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglyceride; HDL, high density lipoprotein; LDL, low density lipoprotein; ApoA, apolipoprotein A; ApoB, apolipoprotein B; “——”, it indicates that there is no unified standard, therefore it was not included in the analysis. Data were presented as mean ± standard deviation for continuous variables and n (%) for categorical variables. Model 1: unadjusted model; Model 2: adjusted for maternal age, pre-pregnancy BMI, literacy, marital status, ethnicity, parity, family history of diabetes mellitus, family history of cardiovascular disease, OGTT-FPG, OGTT-1h, OGTT-2h, smoking (pre-pregnancy and 1 year postpartum), drinking (pre-pregnancy and 1 year postpartum), physical activity and red meat intake at 42 days and 1 year postpartum; Model 2+: Indicating the use of binary logistic regression analysis, with the adjusted variables being the same as in Model 2. *: P < 0.05. 4 Discussion Our findings suggest that women diagnosed with GDM who had poor glycemic control during pregnancy had worse postpartum metabolic health outcomes, however, no significant association was observed in women with late-onset GDM. This observation highlights the importance of glycemic management during pregnancy and suggests that early identification and proactive intervention are crucial for preventing or mitigating future CVD risk in women with GDM. Our study is the first longitudinal perspective study investigated if glucose control during pregnancy could benefit for maternal postpartum metabolic health. Our research indicates that poor glycemic control in GDM during pregnancy is associated with elevated levels of postnatal FPG, TG and TC, which have been demonstrated playing pivotal roles in predicting CVD and T2DM 20 , 21 , 20 – 22 . The evidence regarding benefits of glucose control during pregnancy for GDM women and their offspring is uncertain. Several studies demonstrated that maternal hyperglycemia control reduces the risk of near-term perinatal outcomes 9 , 10 , 23 such as large-for-gestational-age, premature birth, and neonatal hypoglycaemia, but several studies have failed to reach consistent conclusions 24 – 26 . In terms of long-term outcomes for offspring, only one study suggested the benefits of glycemic control during pregnancy for GDM in terms of FPG levels in offspring aged 5 ~ 10 years 27 . No benefits were observed for other outcomes, including fat mass 28 and BMI 27 , 29 , 30 . Interestingly, our results suggest that the impact of glycemic control during pregnancy on postpartum lipid profile was even more broader. This could be explained by the intricate interaction between glucose and lipid metabolism, where insulin resistance—a hallmark of GDM—affects lipid metabolism earlier and more profoundly than glucose homeostasis 31 . During pregnancy, insulin resistance impairs the suppression of lipolysis, leading to increased free fatty acid levels and subsequent hepatic triglyceride synthesis 32 , 33 . These changes result in hypertriglyceridemia, which often persists postpartum despite improvements in glucose regulation 34 . Furthermore, lipid metabolism generally exhibits a slower recovery trajectory compared to glucose metabolism, as the resolution of insulin resistance and low-grade inflammation requires a longer period 35 . This prolonged disturbance in lipid profiles highlights the extended impact of poor glycemic control during pregnancy on postpartum lipid health, underscoring the need for close monitoring and timely interventions targeting lipid abnormalities 36 . Therefore, our study provide new insights into blood glucose management during pregnancy, emphasizing that blood glucose control is essential, for it not only improve glucose itself, but also benefit lipid metabolism. Regarding late-onset GDM, we have not yet observed a significant impact of it on long-term maternal health outcomes. With respect to recent adverse pregnancy outcomes, unlike early-onset and mid-pregnancy GDM, the effects of late-onset GDM appear to be milder, potentially due to the shorter duration of hyperglycemia exposure 37 , 38 . Currently, there is a lack of evidence linking late-onset GDM with long-term metabolic complications in mothers, and our study is the first to report this. Furthermore, the health benefits gained from the intervention for late-GDM remained inconclusive, for a retrospective study showed that targeted interventions in late-onset GDM were associated with improved obstetric and neonatal outcomes 11 , however, another study showed that, compared to pregnant women with normally controlled blood glucose levels throughout their entire pregnancy, late-onset GDM who received standard treatment still face a significantly increased risk of adverse outcomes, particularly the risk of macrosomia 39 . In addition, a study reported that, compared to the management outcomes of early-onset GDM, the benefits of interventions for late-onset GDM, such as reducing adverse perinatal outcomes (birth centiles, rates of preterm birth, neonatal jaundice, neonatal respiratory distress), are less pronounced compared to those observed with early-onset GDM management 40 . However, it is worth noting that the above two studies did not strictly include a control group of patients with late-onset GDM who did not receive intervention. Therefore, the benefits of glycemic control for late-GDM remain unclear, and more research is needed to clarify its unique risks and how it may response to different interventions. 5 Strengths and Limitations Several strengths of our study should be highlighted. Firstly, this is the first study examined the relationship between glycemic management during pregnancy in women with GDM and its impact on postpartum cardiometabolic indices, which provides new evidence and insight for clinic decision maker. Secondly, the prospective longitudinal design strengthens the validity of our results. Additionally, comprehensive confounding factors were adjusted, ensuring the robustness of our findings. However, several limitations should also be noted. Firstly, due to relatively low follow-up rate leading to insufficient sample size, we were unable to explore the impact of different glycemic control standards on long-term metabolism. Two previous studies indicated although stricter control resulted in decreased infant morbidity, it somewhat increased maternal morbidity 25 , 41 . These findings suggest the need for further research to explore the appropriate range of glycemic control during pregnancy for GDM. Moreover, we only obtained a single blood glucose measurement during pregnancy, potentially affecting the stability of results. 6 Conclusions In conclusion, our findings not only further confirm the importance of early recognition of GDM but also emphasize the critical role of subsequent glycemic management during pregnancy in improving postpartum metabolic outcomes. Future research should focus on exploring the long-term effects of different glycemic control standards in improving maternal and offspring long-term health outcomes. Declarations Funding: This work was supported by grants from the National Natural Science Foundation of China (82073564), the Key Project of Outstanding Young Talents Support Program for Colleges and Universities (gxyqZD2021101), and the Provincial Key R&D Programme of Anhui (202104j07020034). The funding agencies had no involvement in the study's design, data collection, analysis, interpretation, or manuscript writing. Availability of data and materials: All data generated or analysed during this study are included in this published article. Data described in the manuscript, code book, and analytic code will be made available upon request pending application and approval. The datasets used and analysed during the current study are available from the corresponding author on reasonable request. Consent for publication: All authors have read the submitted version of the manuscript and agree with its content. Authors' contributions: The study was conceived and designed by FT and BZ. TZ, YD, ZD, LZ, LW, TF, JL, HG, SY, XJ, FY, JR, and CL carried out the data collection and study. TZ and YD wrote the manuscript and performed the analysis. BZ significantly edited the paper and made critical contributions to the interpretation of the data. After reviewing and approving the final manuscript, each author took full responsibility for their individual contributions and ensured that any concerns regarding the integrity or accuracy of any aspect of the work were addressed. Ethics approval and consent to participate: All participants provided written informed consent, and the trial was approved by the Ethics Committee of Anhui Medical University (Approval Number: 20210732). Competing interests: The authors declare that they have no competing interests. This material has been submitted to Diabetology & Metabolic Syndrome only. There is no conflict of interest. All authors have read the submitted version of the manuscript and agree with its content. Acknowledgements: We would like to express our sincere gratitude to the Ma'anshan Maternal and Child Health Care Centre for their assistance with data collection and follow-up. We also extend our heartfelt thanks to all the participants who consented to take part in the follow-up. References Saravanan P. Gestational diabetes: opportunities for improving maternal and child health. Lancet Diabetes Endocrinol . Sep 2020;8(9):793-800. doi:10.1016/s2213-8587(20)30161-3 Saeedi M, Cao Y, Fadl H, Gustafson H, Simmons D. Increasing prevalence of gestational diabetes mellitus when implementing the IADPSG criteria: A systematic review and meta-analysis. Diabetes Res Clin Pract . Feb 2021;172:108642. doi:10.1016/j.diabres.2020.108642 Ye W, Luo C, Huang J, Li C, Liu Z, Liu F. Gestational diabetes mellitus and adverse pregnancy outcomes: systematic review and meta-analysis. Bmj . May 25 2022;377:e067946. doi:10.1136/bmj-2021-067946 Bianco ME, Josefson JL. Hyperglycemia During Pregnancy and Long-Term Offspring Outcomes. Curr Diab Rep . Nov 21 2019;19(12):143. doi:10.1007/s11892-019-1267-6 Wang H, Li N, Chivese T, et al. IDF Diabetes Atlas: Estimation of Global and Regional Gestational Diabetes Mellitus Prevalence for 2021 by International Association of Diabetes in Pregnancy Study Group's Criteria. Diabetes Res Clin Pract . Jan 2022;183:109050. doi:10.1016/j.diabres.2021.109050 Behboudi-Gandevani S, Amiri M, Bidhendi Yarandi R, Ramezani Tehrani F. The impact of diagnostic criteria for gestational diabetes on its prevalence: a systematic review and meta-analysis. Diabetol Metab Syndr . 2019;11:11. doi:10.1186/s13098-019-0406-1 Sweeting A, Hannah W, Backman H, et al. Epidemiology and management of gestational diabetes. Lancet . Jul 13 2024;404(10448):175-192. doi:10.1016/s0140-6736(24)00825-0 Management of Diabetes in Pregnancy: Standards of Care in Diabetes-2024. Diabetes Care . Jan 1 2024;47(Suppl 1):S282-s294. doi:10.2337/dc24-S015 Crowther CA, Hiller JE, Moss JR, McPhee AJ, Jeffries WS, Robinson JS. Effect of treatment of gestational diabetes mellitus on pregnancy outcomes. N Engl J Med . Jun 16 2005;352(24):2477-86. doi:10.1056/NEJMoa042973 Landon MB, Spong CY, Thom E, et al. A multicenter, randomized trial of treatment for mild gestational diabetes. N Engl J Med . Oct 1 2009;361(14):1339-48. doi:10.1056/NEJMoa0902430 Cauldwell M, Chmielewska B, Kaur K, et al. Screening for late-onset gestational diabetes: Are there any clinical benefits? Bjog . Dec 2022;129(13):2176-2183. doi:10.1111/1471-0528.17154 Sgayer I, Odeh M, Wolf MF, et al. The impact on pregnancy outcomes of late-onset gestational diabetes mellitus diagnosed during the third trimester: A systematic review and meta-analysis. Int J Gynaecol Obstet . Jun 2024;165(3):877-888. doi:10.1002/ijgo.15254 Metzger BE, Gabbe SG, Persson B, et al. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care . Mar 2010;33(3):676-82. doi:10.2337/dc09-1848 Association DBoCM. Guideline for the prevention and treatment of type 2 diabetes mellitus in China (2020 edition) (Part 2). Chinese Journal of Practical Internal Medicine . 2021;41(09):757-784. doi:10.19538/j.nk2021090106 Cao J, Gao R, Zhao S, Lu G, Zhao D, Li J. Chinese Guidelines for Prevention and Control of Dyslipidaemia in Adults (2016 Revision). China Circulation Magazine . 2016;31(10):937-953. Craig CL, Marshall AL, Sjöström M, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc . Aug 2003;35(8):1381-95. doi:10.1249/01.Mss.0000078924.61453.Fb Zhong VW, Van Horn L, Greenland P, et al. Associations of Processed Meat, Unprocessed Red Meat, Poultry, or Fish Intake With Incident Cardiovascular Disease and All-Cause Mortality. JAMA Intern Med . Apr 1 2020;180(4):503-512. doi:10.1001/jamainternmed.2019.6969 Yu Y, Arah OA, Liew Z, et al. Maternal diabetes during pregnancy and early onset of cardiovascular disease in offspring: population based cohort study with 40 years of follow-up. Bmj . Dec 4 2019;367:l6398. doi:10.1136/bmj.l6398 Shi W, Huang X, Schooling CM, Zhao JV. Red meat consumption, cardiovascular diseases, and diabetes: a systematic review and meta-analysis. Eur Heart J . Jul 21 2023;44(28):2626-2635. doi:10.1093/eurheartj/ehad336 Sniderman AD, Couture P, Martin SS, et al. Hypertriglyceridemia and cardiovascular risk: a cautionary note about metabolic confounding. J Lipid Res . Jul 2018;59(7):1266-1275. doi:10.1194/jlr.R082271 Bergman RN, Ader M. Free fatty acids and pathogenesis of type 2 diabetes mellitus. Trends Endocrinol Metab . Nov 2000;11(9):351-6. doi:10.1016/s1043-2760(00)00323-4 Zhu XW, Deng FY, Lei SF. Meta-analysis of Atherogenic Index of Plasma and other lipid parameters in relation to risk of type 2 diabetes mellitus. Prim Care Diabetes . Feb 2015;9(1):60-7. doi:10.1016/j.pcd.2014.03.007 Hartling L, Dryden DM, Guthrie A, Muise M, Vandermeer B, Donovan L. Benefits and harms of treating gestational diabetes mellitus: a systematic review and meta-analysis for the U.S. Preventive Services Task Force and the National Institutes of Health Office of Medical Applications of Research. Ann Intern Med . Jul 16 2013;159(2):123-9. doi:10.7326/0003-4819-159-2-201307160-00661 Berezowsky A, Ardestani S, Hiersch L, et al. Glycemic control and neonatal outcomes in twin pregnancies with gestational diabetes mellitus. Am J Obstet Gynecol . Dec 2023;229(6):682.e1-682.e13. doi:10.1016/j.ajog.2023.06.046 Crowther CA, Samuel D, Hughes R, Tran T, Brown J, Alsweiler JM. Tighter or less tight glycaemic targets for women with gestational diabetes mellitus for reducing maternal and perinatal morbidity: A stepped-wedge, cluster-randomised trial. PLoS Med . Sep 2022;19(9):e1004087. doi:10.1371/journal.pmed.1004087 Kariniemi K, Vääräsmäki M, Männistö T, et al. Neonatal outcomes according to different glucose threshold values in gestational diabetes: a register-based study. BMC Pregnancy Childbirth . Apr 12 2024;24(1):271. doi:10.1186/s12884-024-06473-4 Landon MB, Rice MM, Varner MW, et al. Mild gestational diabetes mellitus and long-term child health. Diabetes Care . Mar 2015;38(3):445-52. doi:10.2337/dc14-2159 Manerkar K, Crowther CA, Harding JE, et al. Impact of Gestational Diabetes Detection Thresholds on Infant Growth and Body Composition: A Prospective Cohort Study Within a Randomized Trial. Diabetes Care . Jan 1 2024;47(1):56-65. doi:10.2337/dc23-0464 Gillman MW, Oakey H, Baghurst PA, Volkmer RE, Robinson JS, Crowther CA. Effect of treatment of gestational diabetes mellitus on obesity in the next generation. Diabetes Care . May 2010;33(5):964-8. doi:10.2337/dc09-1810 Hingle M, Blew R, James K, et al. Feasibility and Acceptability of a Type 2 Diabetes Prevention Intervention for Mothers and Children at a Federally Qualified Healthcare Center. J Prim Care Community Health . Jan-Dec 2021;12:21501327211057643. doi:10.1177/21501327211057643 Catalano PM, Kirwan JP, Haugel-de Mouzon S, King J. Gestational diabetes and insulin resistance: role in short- and long-term implications for mother and fetus. J Nutr . May 2003;133(5 Suppl 2):1674s-1683s. doi:10.1093/jn/133.5.1674S Herrera E, Ortega-Senovilla H. Lipid metabolism during pregnancy and its implications for fetal growth. Curr Pharm Biotechnol . 2014;15(1):24-31. doi:10.2174/1389201015666140330192345 Barbour LA, Hernandez TL. Maternal Non-glycemic Contributors to Fetal Growth in Obesity and Gestational Diabetes: Spotlight on Lipids. Curr Diab Rep . May 9 2018;18(6):37. doi:10.1007/s11892-018-1008-2 Lai F, Li Z, Yue S, et al. Early postpartum abnormal glucose metabolism subtype differs according to mid-trimester lipid profile in women with gestational diabetes mellitus. Lipids Health Dis . Aug 24 2021;20(1):91. doi:10.1186/s12944-021-01519-4 Pei L, Xiao H, Lai F, et al. Early postpartum dyslipidemia and its potential predictors during pregnancy in women with a history of gestational diabetes mellitus. Lipids Health Dis . Oct 10 2020;19(1):220. doi:10.1186/s12944-020-01398-1 Yang Z, Li Z, Cheng Y, et al. Association between lipid trajectories during pregnancy and risk of postpartum glucose intolerance after gestational diabetes mellitus: a cohort study. Acta Diabetol . Sep 2022;59(9):1209-1218. doi:10.1007/s00592-022-01905-z Easmin S, Chowdhury TA, Islam MR, et al. Obstetric Outcome in Early and Late Onset Gestational Diabetes Mellitus. Mymensingh Med J . Jul 2015;24(3):450-6. Hosseini E, Janghorbani M, Shahshahan Z. Comparison of risk factors and pregnancy outcomes of gestational diabetes mellitus diagnosed during early and late pregnancy. Midwifery . Nov 2018;66:64-69. doi:10.1016/j.midw.2018.07.017 Tang X, Wei J, Wu S, Wu S. Fasting blood glucose as a screening measure for late-onset gestational diabetes in the third trimester. Bjog . Nov 2024;131(12):1715-1724. doi:10.1111/1471-0528.17897 Candido R, Toffoli B, Manfredi G, et al. Retrospective cohort study on treatment outcomes of early vs late onset gestational diabetes mellitus. Acta Diabetol . Nov 11 2024;doi:10.1007/s00592-024-02405-y Hofer OJ, Alsweiler J, Tran T, Crowther CA. Glycemic control in gestational diabetes and impact on biomarkers in women and infants. Pediatr Res . Aug 2023;94(2):466-476. doi:10.1038/s41390-022-02459-0 Additional Declarations No competing interests reported. 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13:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6639941/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6639941/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88339810,"identity":"9f9c549b-81b1-4caf-bec1-e8e82b07b8d6","added_by":"auto","created_at":"2025-08-05 12:29:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":166276,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant flow chart\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6639941/v1/ece877c00e28f5e13c2e74e1.png"},{"id":88337885,"identity":"0fb3a9a1-99ae-4e0c-a356-efcf24dd37f1","added_by":"auto","created_at":"2025-08-05 12:21:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":613175,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression analysis of association between gestational glycemic control and postpartum metabolic outcomes\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6639941/v1/4a6e310421f18218dba5def1.png"},{"id":88339814,"identity":"5b19704a-1183-4952-8ebb-4113a962576e","added_by":"auto","created_at":"2025-08-05 12:29:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":179629,"visible":true,"origin":"","legend":"\u003cp\u003eLogistic regression analysis of association between gestational glycemic control and postpartum metabolic outcomes\u003c/p\u003e\n\u003cp\u003eNote: Group A, Non-GDM, normal blood glucose (reference); Group B, GDM, normal blood glucose; Group C, GDM, abnormal blood glucose; Group D, Late-onset GDM.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6639941/v1/c370fe885dd24cc537b7fedb.png"},{"id":88342354,"identity":"8df164f5-f63a-424e-9678-4ed86dbd54cd","added_by":"auto","created_at":"2025-08-05 12:53:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1982724,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6639941/v1/c8e96ad4-e60f-49b4-923c-dd3c91df2a07.pdf"},{"id":88339809,"identity":"e6259826-1ceb-4637-b114-2a852879555c","added_by":"auto","created_at":"2025-08-05 12:29:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":35315,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-6639941/v1/ea35a8f5d37319a6570b55ae.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Glycemic Management during Pregnancy and Postpartum Metabolic Health Outcomes among Women with Gestational Diabetes Mellitus","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eWhy did we undertake this study?\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eGestational diabetes mellitus (GDM) is associated with an increased risk of adverse maternal metabolic outcomes postpartum. However, the benefits of glycemic management during pregnancy on maternal postpartum metabolic health remain unclear, particularly in women with late-onset GDM.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eWhat is the specific question(s) we wanted to answer?\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDoes achieving glycemic control during pregnancy improve postpartum metabolic health outcomes? How do postpartum outcomes differ between women with late-onset GDM and those diagnosed earlier in pregnancy?\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eWhat did we find?\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e- Women with GDM who failed to meet glycemic control targets during pregnancy exhibited significantly worse postpartum metabolic outcomes, including higher fasting plasma glucose (FPG), total cholesterol (TC), and triglyceride (TG) levels.\u003c/p\u003e\n\u003cp\u003e- Poor glycemic control during pregnancy also increased the risk of postpartum glucose and lipid abnormalities up to 1 year after delivery.\u003c/p\u003e\n\u003cp\u003e- No significant associations were observed in women with late-onset GDM, suggesting different underlying mechanisms.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eWhat are the implications of our findings?\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOur findings underscore the importance of early diagnosis of GDM and achieving glycemic control during pregnancy to improve postpartum metabolic health. These results support strengthening clinical management strategies and tailoring interventions for GDM, particularly in women with varying onset timings.\u003c/p\u003e"},{"header":"1 Introduction","content":"\u003cp\u003eGestational diabetes mellitus (GDM) refers to impaired glucose tolerance identified for the first time during pregnancy, occurring without any prior diagnosis of diabetes. GDM ranks among the most prevalent complications of pregnancy, impacting roughly 14% of women globally\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. It is widely accepted that GDM has both short-term and long-term health risks on both mothers and their offspring\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Therefore, identifying effective strategies to mitigate the risks of adverse health outcomes is crucial.\u003c/p\u003e\u003cp\u003eIn recent years, substantial revisions to the diagnostic criteria for GDM have resulted in a significant rise in the number of women identified with this condition\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. This trend has placed unprecedented strain on the healthcare system, increasing resource consumption and costs, while also imposing substantial psychological and financial burdens on individuals and their families\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. While relationship between GDM and adverse maternal-fetal pregnancy outcomes, as well as long-term health risks have been well documented\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, a key question remains: could women and their offspring benefit from the subsequent interventions after being identified under these broader diagnostic criteria? American Diabetes Association emphasizes the placement of blood glucose control at the forefront of GDM management during pregnancy\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Research has demonstrated that controlling maternal hyperglycemia through appropriate treatment can lower the likelihood of large-for-gestational-age infants and enhance various key maternal and perinatal health outcomes\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. However, regarding maternal postpartum health outcomes, only one randomized controlled trial demonstrated dietary management, monitoring blood glucose levels and administering insulin therapy (if necessary) could significantly reduce incidence of postpartum depressive symptoms (control group: 50%, intervention group: 23%)\u003csup\u003e9\u003c/sup\u003e. While no study has demonstrated if glycemic control during pregnancy could bring favorable maternal long-term metabolic outcomes. Additionally, the adverse effects of late-onset GDM is of great research interesting, which refers to those who have normal glucose during the second trimester but have abnormal blood glucose in the third trimester\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. A meta-analysis has shown that late-onset GDM is linked to a higher likelihood of unfavorable perinatal outcomes\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, however, its impact on long-term maternal metabolic health has not been previously documented.\u003c/p\u003e\u003cp\u003eTherefore, based on an ongoing cohort study in China which includes 1108 women, we categorized women into four groups according to their GDM diagnosis status in the second trimester and the glycemic control status recorded during the third trimester and compared their postpartum metabolic indicators to assess if the postpartum metabolic health outcomes of individuals diagnosed with GDM who met the glycemic control target (including those with late-onset GDM) during pregnancy triumph over those not. The ultimate goal is to provide crucial scientific evidence for the formulation of clinical practice guidelines and public health policies.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Participants\u003c/h2\u003e\u003cp\u003e Our current study was conducted as part of an ongoing GDM Specialized cohort study, which recruited participants between June 2021 and December 2022 at the Ma\u0026rsquo;anshan Maternal and Child Health Care Center in Ma\u0026rsquo;anshan City, Anhui Province, China. The study aims to evaluate the impact of GDM on perinatal and long-term postnatal health outcomes, as well as to investigate the role of genetic and environmental risk factors in the development of GDM and its progression to metabolic diseases. Between 24\u0026ndash;28 weeks of gestation, women diagnosed with GDM and those not were enrolled at a ratio of 1:2, and a total 1108 women were included, of which 358 were GDM cases and 750 were controls. Participants were routinely followed up at 32 weeks of gestation, 42 days postpartum, 1 year postpartum, and annually thereafter. The study included participants who met the following criteria: (1) gestational age between 24 and 28 weeks; (2) age of 18 years or older; (3) no communication barriers and the ability to independently complete a questionnaire; and (4) willingness to attend prenatal check-ups and deliver at the Ma\u0026rsquo;anshan Maternal and Child Health Care Center. Participants were excluded if they had visual or cognitive impairments or were involved in other ongoing clinical studies.\u003c/p\u003e\u003cp\u003e The study received approval from the Ethics Committee of Anhui Medical University (Approval No. 20210732). All procedures adhered to relevant ethical standards and regulations. Written informed consent was obtained from all participants, and their personal information was handled with strict confidentiality.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Criteria for hyperglycaemia\u003c/h2\u003e\u003cp\u003eGDM was diagnosed between 24 and 28 weeks of gestation using a 75-g oral glucose tolerance test (OGTT). The diagnosis was made when any of the following criteria was met: fasting plasma glucose (FPG)\u0026thinsp;\u0026ge;\u0026thinsp;5.1 mmol/L, 1-hour post-glucose plasma glucose (PG)\u0026thinsp;\u0026ge;\u0026thinsp;10.0 mmol/L, or 2-hour post-glucose PG\u0026thinsp;\u0026ge;\u0026thinsp;8.5 mmol/L\u003csup\u003e13\u003c/sup\u003e. In late pregnancy (around 38 weeks), evaluations were conducted in accordance with the Chinese guidelines for the prevention and management of type 2 diabetes, FPG\u0026thinsp;\u0026ge;\u0026thinsp;5.1 mmol/L is considered as abnormal glycemia in GDM\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. If blood glucose is normal in early and mid-pregnancy but exceeds 5.1 mmol/L in late pregnancy\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, the participant was considered as late-onset GDM in our study. Mid- and late-pregnancy blood glucose data for participants were extracted from the electronic medical record system.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Criteria for Subgroups\u003c/h2\u003e\u003cp\u003e Participants were categorized into four groups based on the criteria for GDM and following glycemic management in late pregnancy, as specified in the Chinese guidelines for the prevention and management of T2DM. A threshold of 5.1 mmol/L was used to distinguish between normal and abnormal glycemic control\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The groups were as follows: (1) Non-GDM with normal glycemic control (control group) (FPG\u0026thinsp;\u0026lt;\u0026thinsp;5.1 mmol/L); (2) GDM with normal glycemic control (FPG\u0026thinsp;\u0026lt;\u0026thinsp;5.1 mmol/L); (3) GDM with abnormal glycemic control (FPG\u0026thinsp;\u0026ge;\u0026thinsp;5.1 mmol/L); and (4) Late-onset GDM (FPG\u0026thinsp;\u0026ge;\u0026thinsp;5.1 mmol/L during the third trimester).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Outcomes\u003c/h2\u003e\u003cp\u003eWomen diagnosed with GDM take a 75-g OGTT 42 days postpartum. Outcomes at 42 days postpartum was fasting plasma glucose [FPG], 1-hour post-glucose plasma glucose [1h PG], and 2-hour post-glucose plasma glucose [2h PG] and outcomes at 1 year postpartum were FPG, total cholesterol [TC], triglycerides [TG], high-density lipoprotein [HDL], low-density lipoprotein [LDL], apolipoprotein A [ApoA], and apolipoprotein B [ApoB] and physical examination results including diastolic blood pressure (DBP), systolic blood pressure (SBP), hip circumference, and waist circumference. Biochemical indicators (blood glucose and blood lipids) were retrieved from the electronic medical record system, while physical indicators were collected on-site by the cohort working staff. Blood pressure was assessed by employing a conventional mercury sphygmomanometer through the cuff inflation technique. Prior to measurement, participants were instructed to rest quietly for at least 10 minutes in a calm and relaxed state. Blood pressure was recorded on the right upper arm while the participant remained seated. Measurements were taken twice consecutively, with an interval of at least 30 seconds between each measurement. The individual\u0026rsquo;s blood pressure value was determined by calculating the average of the two measurements.\u003c/p\u003e\u003cp\u003eAn abnormal metabolic condition during the postpartum period was characterized as follows: (1) Abnormal blood glucose: FPG\u0026thinsp;\u0026ge;\u0026thinsp;5.6 mmol/L, 2h PG\u0026thinsp;\u0026ge;\u0026thinsp;7.8 mmol/L\u003csup\u003e14\u003c/sup\u003e; (2) Abnormal lipid levels were identified based on the criteria outlined in the 2016 Guidelines for the Prevention and Control of Dyslipidemia in Adults in China\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. These abnormalities were categorized into four distinct types: 1) hypercholesterolemia, defined as TC\u0026thinsp;\u0026ge;\u0026thinsp;5.2 mmol/L; 2) hypertriglyceridemia, defined as TG\u0026thinsp;\u0026ge;\u0026thinsp;1.7 mmol/L; 3) low high-density lipoproteinemia, where HDL\u0026thinsp;\u0026le;\u0026thinsp;1.0 mmol/L; and 4) high low-density lipoproteinemia, characterized by LDL\u0026thinsp;\u0026ge;\u0026thinsp;3.4 mmol/L. An individual had an abnormal level of glucose in any period would be categorized as had abnormal glucose. And the blood lipid abnormality is defined as any of the lipid indicators exceeds the normal range.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Covariates\u003c/h2\u003e\u003cp\u003eSelf-administered questionnaires guided by trained interviewers were used to collect comprehensive information at baseline and during follow-up. Data collected included maternal socio-demographic characteristics and lifestyle factors during pregnancy, such as maternal age, pre-pregnancy body mass index (BMI), and educational attainment (categorized as middle school or lower, high school, junior college, or higher), parity (0 or \u0026ge;\u0026thinsp;1), place of residence (urban or rural), smoking and alcohol consumption during pregnancy (yes or no), family history of diabetes or cardiovascular disease (CVD) (immediate family members, typically parents), and physical activity. The level of physical activity was evaluated through the use of the International Physical Activity Questionnaire (IPAQ)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, a validated and reliable tool, with results categorized as low, moderate, or high activity levels. Red meat intake was measured using the Dietary Frequency Questionnaire (Food Frequency Questionnaire, FFQ)\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, which records the frequency (daily, weekly, monthly, or yearly), number of servings, and grams of food intake. OGTT, the OGTT values measured between the 24 and 28 weeks of gestation, including FPG, 1h PG, and 2h PG. Pre-pregnancy BMI, calculated as [weight (kg) / height (m)\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e], was derived from self-reported measurements of weight and height. prior to pregnancy. Covariates were selected based on the results of univariable analyses (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and prior literature\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e demonstrating their significant associations with outcomes such as GDM and CVD.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e\u003cp\u003eFor continuous variables with a normal distribution, data were presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and comparisons of baseline demographic and clinical characteristics between independent samples were conducted using t-tests. Count data were presented as frequencies (%) and compared between groups using the chi-square (χ\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) test. Multivariate linear and logistic regression were applied wherever was appropriate to assess the associations of glycemic management status during pregnancy with postpartum physical (waist and hip circumference) and metabolic indicators (blood pressure, FPG, TC, TG, HDL, LDL, ApoA, and ApoB). Adjusted factors were selected based on previous literature\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, including maternal age, pre-pregnancy BMI, education level, marital status, ethnicity, parity, family history of diabetes, family history of cardiovascular disease, smoking (pre-pregnancy and at 1 year postpartum), alcohol consumption (pre-pregnancy and at 1 year postpartum), and physical activity and red meat intake at 42 days and 1 year postpartum. We further conducted a comparative analysis between GDM with abnormal glycemic control and GDM with normal glycemic control.\u003c/p\u003e\u003cp\u003eA 2-sided P value of less than 0.05 was set as the level of significance. Statistical analyses were conducted with R 4.2.2, GraphPad Prism 8, and SPSS 23.0 software.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Study Participant Disposition\u003c/h2\u003e\n \u003cp\u003eAt baseline, we included a total of 1108 women with a mean age of 30.85 (3.92) years, among which 358 (32.3%) were GDM cases and 750 (67.7%) were Non-GDM controls. Comparing the basic characteristics of the two groups, we found that the mean age (31.94 (3.71) years VS 30.33 (3.90) years), the mean BMI ( 23.09 (3.38) kg/m\u003csup\u003e2\u003c/sup\u003e VS 21.77 (3.19) kg/m\u003csup\u003e2\u003c/sup\u003e), and the percentage of family history of diabetes mellitus (32.4% VS 22.7%) in the GDM group were higher than that in the Non-GDM group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant differences were observed in other characteristics such as educational level, smoking and alcohol consumption. Detailed characteristics of participant are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of basic information of study subjects at baseline\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-GDM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGDM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003et/\u0026chi;2\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1108)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;750)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;358)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.85 (3.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.33 (3.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.94 (3.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.20 (3.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.77 (3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.09 (3.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthnicity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHan individuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1089 (98.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e737 (98.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e352 (98.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1095 (99.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e740 (98.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e355 (99.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation level (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e173 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e109 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e157 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73 (20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJunior college or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e696 (63.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e477 (63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e219 (61.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNature of work (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMental labor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e601 (54.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e406 (54.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e195 (54.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManual labor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e433 (39.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e289 (38.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e144 (40.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual household income (yuan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;50, 000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e164 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50, 000\u0026thinsp;~\u0026thinsp;99, 000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e383 (34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e268 (35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115 (32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100, 000\u0026thinsp;~\u0026thinsp;199, 000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e401 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e275 (36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e126 (35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200, 000ཞ299, 000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;300, 000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1066 (96.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e724 (96.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e342 (95.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrinking (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e926 (83.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e621 (82.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e305 (85.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSometimes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e129 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOften\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlways\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondhand smoke (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e503 (45.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e339 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e164 (45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSometimes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e452 (40.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e304 (40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e148 (41.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOften\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlways\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistory of adverse pregnancy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e398 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e261 (34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e137 (38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eMissing values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e153 (13.8)\u003c/p\u003e\n \u003cp\u003e557 (50.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99 (13.2)\u003c/p\u003e\n \u003cp\u003e390 (52.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54 (15.1)\u003c/p\u003e\n \u003cp\u003e167 (46.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFamily history of diabetes (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e822 (74.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e580 (77.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e242 (67.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e286 (25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e170 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFamily history of cardiovascular disease (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e861 (77.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e580 (77.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e281 (78.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e246 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e170 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 99.9145%;\"\u003eData were presented as mean (standard deviation) for continuous variables and n (%) for categorical variables.\u003cbr\u003eAbbreviations: BMI, pre-pregnancy body mass index. *: P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003cbr\u003eNature of work, annual household income and family history of cardiovascular disease were missing one person respectively. History of adverse pregnancy was collected at a later stage, so there was no data on the previous period.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eBy the late stage of pregnancy, we successfully collected blood glucose data from a total of 989 women. Subsequently, during the 42-day postpartum follow-up, we collected 642 questionnaires and successfully obtained blood glucose data from 466 participants. Further tracking until one year postpartum, 736 individuals completed questionnaires, 617 individuals completed a physical examination, 583 completed blood pressure measurements, and 376 underwent comprehensive blood glucose and lipid testing (for details, see Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean follow-up duration at 42 days postpartum was 44.51 (7.17) days. The overall abnormality rate of blood glucose levels postpartum is 9.3%. The blood glucose abnormality rate at 42 days postpartum was 10.8% in the GDM group and 6.0% in the Non-GDM group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.054). The mean follow-up duration at 1 year postpartum was 442.52 (88.63) days, and the blood glucose abnormality rates were 9.7% and 3.7% in the GDM and Non-GDM groups, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.098), and the blood lipid abnormality rate was 16.7% and 15.3% in the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.510). Among them, the abnormality rate for TC, TG, HDL, and LDL were 16.1%, 15.2%, 7.3%, and 13.7%. And the TC, TG, HDL, LDL abnormality rates were (14.6% \u0026amp; 18.7%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.290), (10.0% \u0026amp; 24.0%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), (5.4% \u0026amp; 10.6%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049), (12.4% \u0026amp; 16.0%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.301) in the GDM and Non-GDM groups.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Comparing Postnatal Metabolic Outcomes between Glycaemic Control Status during Pregnancy\u003c/h2\u003e\n \u003cp\u003eIn the fully adjusted model, linear regression analysis indicated that compared to the Non-GDM, women diagnosed with GDM who had abnormal glycemia during pregnancy had higher FPG (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.24, [95% \u003cem\u003eCI\u003c/em\u003e 0.05 to 0.43], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012) at 42-day postpartum, and elevated TC (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.37, [95% \u003cem\u003eCI\u003c/em\u003e 0.05 to 0.69], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023) and TG (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.42, [95% \u003cem\u003eCI\u003c/em\u003e 0.57 to 10.26], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029) levels at 1-year postpartum. No significant elevated level of any indicators were observed among women with late-onset GDM (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Besides, compared with GDM with normal glycemia control during pregnancy, GDM who had abnormal glycemia control had elevated TC (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.48, [95% \u003cem\u003eCI\u003c/em\u003e 0.16 to 0.80], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) and LDL (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.30, [95% \u003cem\u003eCI\u003c/em\u003e 0.04 to 0.56], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024) levels at 1-year postpartum.\u003c/p\u003e\n \u003cp\u003eLogistic regression analysis revealed that, compared to the Non-GDM, women diagnosed with GDM having abnormal blood glucose control during pregnancy had an increased risk of postpartum (42 days and 1 year combined) blood glucose abnormality (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.22, [95% \u003cem\u003eCI\u003c/em\u003e 1.66 to 16.38], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) and a higher risk of postpartum TG abnormality (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.43, [95% \u003cem\u003eCI\u003c/em\u003e 1.01 to 5.86], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048). Similarly, no significant increased risk of any metabolic abnormality were observed among women with late-onset GDM (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Table S2). Compared with GDM with normal glycemia control during pregnancy, GDM who had abnormal glycemia had increased risks of 42 days postpartum 2h PG abnormality (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.77, [95% \u003cem\u003eCI\u003c/em\u003e 1.02 to 7.53], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045) and 1 year postpartum TC abnormality (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.97, [95% \u003cem\u003eCI\u003c/em\u003e 1.08 to 8.18], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation between glycemic control and postpartum metabolic outcomes among women with GDM\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIndicators\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGDM, normal glycaemic control\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGDM, abnormal glycaemic control\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 2+\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta; (95%CI)\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta; (95%CI)\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOR (95%CI)\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\"\u003e\n \u003cp\u003e42 days postpartum\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFPG (n\u0026thinsp;=\u0026thinsp;187)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119 (4.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (5.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13 (-0.05, 0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11 (-0.09, 0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.59 (0.86, 7.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1h PG (n\u0026thinsp;=\u0026thinsp;124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80 (8.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (8.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.13 (-0.73, 0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00 (-0.75, 0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2h PG (n\u0026thinsp;=\u0026thinsp;113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76 (6.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (7.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32 (-0.34, 0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67 (-0.18, 1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.77 (1.02, 7.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\"\u003e\n \u003cp\u003e1 year postpartum\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFPG (n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83 (4.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (4.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15 (-0.06, 0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11 (-0.12, 0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.25 (0.52, 9.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBP (n\u0026thinsp;=\u0026thinsp;194)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119 (108.48\u0026thinsp;\u0026plusmn;\u0026thinsp;12.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75 (109.93\u0026thinsp;\u0026plusmn;\u0026thinsp;13.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45 (-2.11, 5.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72 (-3.24, 4.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.20 (0.28, 64.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDBP (n\u0026thinsp;=\u0026thinsp;194)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119 (73.76\u0026thinsp;\u0026plusmn;\u0026thinsp;9.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75 (77.02\u0026thinsp;\u0026plusmn;\u0026thinsp;8.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.26 (0.60, 5.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.50 (-0.42, 5.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91 (0.21, 3.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaist (n\u0026thinsp;=\u0026thinsp;209)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128 (71.43\u0026thinsp;\u0026plusmn;\u0026thinsp;25.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81 (74.12\u0026thinsp;\u0026plusmn;\u0026thinsp;22.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.69 (-3.76, 9.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21 (-5.99, 8.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHips (n\u0026thinsp;=\u0026thinsp;209)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128 (86.75\u0026thinsp;\u0026plusmn;\u0026thinsp;29.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81 (90.39\u0026thinsp;\u0026plusmn;\u0026thinsp;26.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.63 (-3.96, 11.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.25 (-6.42, 10.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC (n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83 (4.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (4.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46 (0.18, 0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48 (0.16, 0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.97 (1.08, 8.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG (n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83 (1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (4.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.49 (-0.49, 7.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.20 (-1.84, 8.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55 (0.63, 3.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL (n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83 (1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01 (-0.10, 0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00 (-0.09, 0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77 (0.19, 3.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL (n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83 (2.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (2.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29 (0.06, 0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30 (0.04, 0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65 (0.58, 4.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApoA (n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83 (1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03 (-0.05, 0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02 (-0.07, 0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApoB (n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83 (0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07 (0.01, 0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07 (-0.00, 0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" style=\"width: 99.9145%;\"\u003eAbbreviation: FPG, fasting plasma glucose; 1h PG, 1-hour post-glucose plasma glucose; 2h PG, 2-hour post-glucose plasma glucose; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglyceride; HDL, high density lipoprotein; LDL, low density lipoprotein; ApoA, apolipoprotein A; ApoB, apolipoprotein B; \u0026ldquo;\u0026mdash;\u0026mdash;\u0026rdquo;, it indicates that there is no unified standard, therefore it was not included in the analysis.\u003cbr\u003eData were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for continuous variables and n (%) for categorical variables.\u003cbr\u003eModel 1: unadjusted model; Model 2: adjusted for maternal age, pre-pregnancy BMI, literacy, marital status, ethnicity, parity, family history of diabetes mellitus, family history of cardiovascular disease, OGTT-FPG, OGTT-1h, OGTT-2h, smoking (pre-pregnancy and 1 year postpartum), drinking (pre-pregnancy and 1 year postpartum), physical activity and red meat intake at 42 days and 1 year postpartum; Model 2+: Indicating the use of binary logistic regression analysis, with the adjusted variables being the same as in Model 2.\u003cbr\u003e*: P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eOur findings suggest that women diagnosed with GDM who had poor glycemic control during pregnancy had worse postpartum metabolic health outcomes, however, no significant association was observed in women with late-onset GDM. This observation highlights the importance of glycemic management during pregnancy and suggests that early identification and proactive intervention are crucial for preventing or mitigating future CVD risk in women with GDM.\u003c/p\u003e\u003cp\u003eOur study is the first longitudinal perspective study investigated if glucose control during pregnancy could benefit for maternal postpartum metabolic health. Our research indicates that poor glycemic control in GDM during pregnancy is associated with elevated levels of postnatal FPG, TG and TC, which have been demonstrated playing pivotal roles in predicting CVD and T2DM\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The evidence regarding benefits of glucose control during pregnancy for GDM women and their offspring is uncertain. Several studies demonstrated that maternal hyperglycemia control reduces the risk of near-term perinatal outcomes\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e such as large-for-gestational-age, premature birth, and neonatal hypoglycaemia, but several studies have failed to reach consistent conclusions\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In terms of long-term outcomes for offspring, only one study suggested the benefits of glycemic control during pregnancy for GDM in terms of FPG levels in offspring aged 5\u0026thinsp;~\u0026thinsp;10 years\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. No benefits were observed for other outcomes, including fat mass\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003eand BMI\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eInterestingly, our results suggest that the impact of glycemic control during pregnancy on postpartum lipid profile was even more broader. This could be explained by the intricate interaction between glucose and lipid metabolism, where insulin resistance\u0026mdash;a hallmark of GDM\u0026mdash;affects lipid metabolism earlier and more profoundly than glucose homeostasis\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. During pregnancy, insulin resistance impairs the suppression of lipolysis, leading to increased free fatty acid levels and subsequent hepatic triglyceride synthesis\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. These changes result in hypertriglyceridemia, which often persists postpartum despite improvements in glucose regulation\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Furthermore, lipid metabolism generally exhibits a slower recovery trajectory compared to glucose metabolism, as the resolution of insulin resistance and low-grade inflammation requires a longer period\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. This prolonged disturbance in lipid profiles highlights the extended impact of poor glycemic control during pregnancy on postpartum lipid health, underscoring the need for close monitoring and timely interventions targeting lipid abnormalities\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Therefore, our study provide new insights into blood glucose management during pregnancy, emphasizing that blood glucose control is essential, for it not only improve glucose itself, but also benefit lipid metabolism.\u003c/p\u003e\u003cp\u003eRegarding late-onset GDM, we have not yet observed a significant impact of it on long-term maternal health outcomes. With respect to recent adverse pregnancy outcomes, unlike early-onset and mid-pregnancy GDM, the effects of late-onset GDM appear to be milder, potentially due to the shorter duration of hyperglycemia exposure\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Currently, there is a lack of evidence linking late-onset GDM with long-term metabolic complications in mothers, and our study is the first to report this. Furthermore, the health benefits gained from the intervention for late-GDM remained inconclusive, for a retrospective study showed that targeted interventions in late-onset GDM were associated with improved obstetric and neonatal outcomes\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, however, another study showed that, compared to pregnant women with normally controlled blood glucose levels throughout their entire pregnancy, late-onset GDM who received standard treatment still face a significantly increased risk of adverse outcomes, particularly the risk of macrosomia\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. In addition, a study reported that, compared to the management outcomes of early-onset GDM, the benefits of interventions for late-onset GDM, such as reducing adverse perinatal outcomes (birth centiles, rates of preterm birth, neonatal jaundice, neonatal respiratory distress), are less pronounced compared to those observed with early-onset GDM management\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. However, it is worth noting that the above two studies did not strictly include a control group of patients with late-onset GDM who did not receive intervention. Therefore, the benefits of glycemic control for late-GDM remain unclear, and more research is needed to clarify its unique risks and how it may response to different interventions.\u003c/p\u003e"},{"header":"5 Strengths and Limitations","content":"\u003cp\u003eSeveral strengths of our study should be highlighted. Firstly, this is the first study examined the relationship between glycemic management during pregnancy in women with GDM and its impact on postpartum cardiometabolic indices, which provides new evidence and insight for clinic decision maker. Secondly, the prospective longitudinal design strengthens the validity of our results. Additionally, comprehensive confounding factors were adjusted, ensuring the robustness of our findings. However, several limitations should also be noted. Firstly, due to relatively low follow-up rate leading to insufficient sample size, we were unable to explore the impact of different glycemic control standards on long-term metabolism. Two previous studies indicated although stricter control resulted in decreased infant morbidity, it somewhat increased maternal morbidity\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. These findings suggest the need for further research to explore the appropriate range of glycemic control during pregnancy for GDM. Moreover, we only obtained a single blood glucose measurement during pregnancy, potentially affecting the stability of results.\u003c/p\u003e"},{"header":"6 Conclusions","content":"\u003cp\u003eIn conclusion, our findings not only further confirm the importance of early recognition of GDM but also emphasize the critical role of subsequent glycemic management during pregnancy in improving postpartum metabolic outcomes. Future research should focus on exploring the long-term effects of different glycemic control standards in improving maternal and offspring long-term health outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was supported by grants from the National Natural Science Foundation of China (82073564), the Key Project of Outstanding Young Talents Support Program for Colleges and Universities (gxyqZD2021101), and the Provincial Key R\u0026amp;D Programme of Anhui (202104j07020034). The funding agencies had no involvement in the study\u0026apos;s design, data collection, analysis, interpretation, or manuscript writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e All data generated or analysed during this study are included in this published article. Data described in the manuscript, code book, and analytic code will be made available upon request pending application and approval.\u0026nbsp;The datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e All authors have read the submitted version of the\u0026nbsp;\u003c/p\u003e\n\u003cp\u003emanuscript and agree with its content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e The study was conceived and designed by FT and BZ. TZ, YD, ZD, LZ, LW, TF, JL, HG, SY, XJ, FY, JR, and CL carried out the data collection and study. TZ and YD wrote the manuscript and performed the analysis. BZ significantly edited the paper and made critical contributions to the interpretation of the data. After reviewing and approving the final manuscript, each author took full responsibility for their individual contributions and ensured that any concerns regarding the integrity or accuracy of any aspect of the work were addressed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e All participants provided written informed consent, and the trial was approved by the Ethics Committee of Anhui Medical University (Approval Number: 20210732).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eThis material has been submitted to Diabetology \u0026amp; Metabolic Syndrome only. There is no conflict of interest. All authors have read the submitted version of the manuscript and agree with its content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e We would like to express our sincere gratitude to the Ma\u0026apos;anshan Maternal and Child Health Care Centre for their assistance with data collection and follow-up. We also extend our heartfelt thanks to all the participants who consented to take part in the follow-up.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSaravanan P. Gestational diabetes: opportunities for improving maternal and child health. \u003cem\u003eLancet Diabetes Endocrinol\u003c/em\u003e. Sep 2020;8(9):793-800. doi:10.1016/s2213-8587(20)30161-3\u003c/li\u003e\n\u003cli\u003eSaeedi M, Cao Y, Fadl H, Gustafson H, Simmons D. Increasing prevalence of gestational diabetes mellitus when implementing the IADPSG criteria: A systematic review and meta-analysis. \u003cem\u003eDiabetes Res Clin Pract\u003c/em\u003e. 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Benefits and harms of treating gestational diabetes mellitus: a systematic review and meta-analysis for the U.S. Preventive Services Task Force and the National Institutes of Health Office of Medical Applications of Research. \u003cem\u003eAnn Intern Med\u003c/em\u003e. Jul 16 2013;159(2):123-9. doi:10.7326/0003-4819-159-2-201307160-00661\u003c/li\u003e\n\u003cli\u003eBerezowsky A, Ardestani S, Hiersch L, et al. Glycemic control and neonatal outcomes in twin pregnancies with gestational diabetes mellitus. \u003cem\u003eAm J Obstet Gynecol\u003c/em\u003e. Dec 2023;229(6):682.e1-682.e13. doi:10.1016/j.ajog.2023.06.046\u003c/li\u003e\n\u003cli\u003eCrowther CA, Samuel D, Hughes R, Tran T, Brown J, Alsweiler JM. Tighter or less tight glycaemic targets for women with gestational diabetes mellitus for reducing maternal and perinatal morbidity: A stepped-wedge, cluster-randomised trial. \u003cem\u003ePLoS Med\u003c/em\u003e. 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Jan 1 2024;47(1):56-65. doi:10.2337/dc23-0464\u003c/li\u003e\n\u003cli\u003eGillman MW, Oakey H, Baghurst PA, Volkmer RE, Robinson JS, Crowther CA. Effect of treatment of gestational diabetes mellitus on obesity in the next generation. \u003cem\u003eDiabetes Care\u003c/em\u003e. May 2010;33(5):964-8. doi:10.2337/dc09-1810\u003c/li\u003e\n\u003cli\u003eHingle M, Blew R, James K, et al. Feasibility and Acceptability of a Type 2 Diabetes Prevention Intervention for Mothers and Children at a Federally Qualified Healthcare Center. \u003cem\u003eJ Prim Care Community Health\u003c/em\u003e. Jan-Dec 2021;12:21501327211057643. doi:10.1177/21501327211057643\u003c/li\u003e\n\u003cli\u003eCatalano PM, Kirwan JP, Haugel-de Mouzon S, King J. Gestational diabetes and insulin resistance: role in short- and long-term implications for mother and fetus. \u003cem\u003eJ Nutr\u003c/em\u003e. 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Nov 2018;66:64-69. doi:10.1016/j.midw.2018.07.017\u003c/li\u003e\n\u003cli\u003eTang X, Wei J, Wu S, Wu S. Fasting blood glucose as a screening measure for late-onset gestational diabetes in the third trimester. \u003cem\u003eBjog\u003c/em\u003e. Nov 2024;131(12):1715-1724. doi:10.1111/1471-0528.17897\u003c/li\u003e\n\u003cli\u003eCandido R, Toffoli B, Manfredi G, et al. Retrospective cohort study on treatment outcomes of early vs late onset gestational diabetes mellitus. \u003cem\u003eActa Diabetol\u003c/em\u003e. Nov 11 2024;doi:10.1007/s00592-024-02405-y\u003c/li\u003e\n\u003cli\u003eHofer OJ, Alsweiler J, Tran T, Crowther CA. Glycemic control in gestational diabetes and impact on biomarkers in women and infants. \u003cem\u003ePediatr Res\u003c/em\u003e. Aug 2023;94(2):466-476. doi:10.1038/s41390-022-02459-0 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gestational diabetes mellitus, Glycemic management during pregnancy, Postpartum, Metabolic indicators","lastPublishedDoi":"10.21203/rs.3.rs-6639941/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6639941/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eGlycemic management is standard for gestational diabetes mellitus (GDM), yet its impact on postpartum metabolic health, especially in late-onset GDM, remains uncertain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective \u003c/strong\u003eTo assess whether achieving glycemic control during pregnancy improves postpartum metabolic health compared to those who do not.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003eFrom June 2021 to December 2022, 358 gestational diabetes mellitus (GDM) cases and 750 controls were recruited at 24~28 weeks of gestation from Ma'anshan Maternal and Child Health Care Center, China. Participants were categorized into four groups based on third-trimester fasting plasma glucose (FPG): 1) Non-GDM, 2) GDM with normal glycemic control (FPG \u0026lt; 5.1 mmol/L), 3) GDM with abnormal glycemic control, and 4) late-onset GDM. Follow-ups at 42 days and 1 year postpartum included questionnaires, physical examinations, and metabolic measurements. Multivariate regression analyzed associations between glycemic control and postpartum outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eAmong 642 and 736 participants followed at 42 days and 1 year postpartum, GDM with abnormal glycemic control had increased risks of blood glucose (\u003cem\u003eOR\u003c/em\u003e= 5.22, [95% \u003cem\u003eCI\u003c/em\u003e 1.66 to 16.38], \u003cem\u003eP\u003c/em\u003e=0.005) and TG abnormalities (\u003cem\u003eOR\u003c/em\u003e= 2.43, [95% \u003cem\u003eCI\u003c/em\u003e 1.01 to 5.85], \u003cem\u003eP\u003c/em\u003e=0.048). No significant associations were found for GDM with normal glycemic control or late-onset GDM. Compared to GDM with normal control, abnormal control increased risks of 2-hour glucose (\u003cem\u003eOR\u003c/em\u003e= 2.77, [95% \u003cem\u003eCI\u003c/em\u003e 1.02 to 7.53], \u003cem\u003eP\u003c/em\u003e=0.045) and TC abnormalities (\u003cem\u003eOR\u003c/em\u003e= 2.97, [95% \u003cem\u003eCI\u003c/em\u003e 1.08 to 8.18], \u003cem\u003eP\u003c/em\u003e=0.035).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003eGlycemic management during pregnancy improves postpartum metabolic outcomes, highlighting the importance of GDM diagnosis and subsequent glycemic control.\u003c/p\u003e","manuscriptTitle":"Association between Glycemic Management during Pregnancy and Postpartum Metabolic Health Outcomes among Women with Gestational Diabetes Mellitus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-05 12:21:42","doi":"10.21203/rs.3.rs-6639941/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-21T15:32:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-12T12:59:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-06T09:07:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245606227482616843761547145010628032897","date":"2025-08-06T02:36:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"292447243092958786880566270718201532900","date":"2025-08-03T02:11:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-30T03:43:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-15T12:55:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-15T12:50:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Diabetology \u0026 Metabolic Syndrome","date":"2025-05-11T13:39:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fca2f5f7-e639-4733-b12e-ad18831df998","owner":[],"postedDate":"August 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-15T13:38:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-05 12:21:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6639941","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6639941","identity":"rs-6639941","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}